CN110378949B - Starch granule distribution analysis device and method thereof - Google Patents
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- 229920002472 Starch Polymers 0.000 title claims abstract description 350
- 235000019698 starch Nutrition 0.000 title claims abstract description 350
- 239000008107 starch Substances 0.000 title claims abstract description 350
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- 238000004458 analytical method Methods 0.000 title claims abstract description 84
- 238000000034 method Methods 0.000 title claims description 12
- 241000209094 Oryza Species 0.000 claims abstract description 175
- 235000007164 Oryza sativa Nutrition 0.000 claims abstract description 175
- 235000009566 rice Nutrition 0.000 claims abstract description 175
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- 210000002421 cell wall Anatomy 0.000 description 17
- 238000010186 staining Methods 0.000 description 15
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- 239000000284 extract Substances 0.000 description 8
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- 238000001303 quality assessment method Methods 0.000 description 1
- 229940100486 rice starch Drugs 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/60—Analysis of geometric attributes
- G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
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- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/10061—Microscopic image from scanning electron microscope
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Abstract
The invention provides a starch grain distribution analysis device for analyzing and processing images of section slices of dyed rice, which comprises: an image reconstruction module for extracting image components concerning starch grains based on color distribution of pixels in the image to create a starch grain distribution image; and a starch distribution determining module for determining the starch content distribution of the rice cross section based on the distribution of starch granule pixels in the starch granule distribution image.
Description
Technical Field
The invention relates to a biological analysis device, in particular to a rice cross section starch granule distribution analysis device and a method thereof.
Background
The amount and proportion of starch in rice determines the nutritional value of the rice. In the process of quality analysis based on rice starch, the starch grain distribution of the cross section is a common analysis method. After the stained sections of the rice are obtained, they are placed under a microscope to observe the distribution of starch grains, so that morphological studies are performed with the distribution of starch grains, and the starch content of the rice can be estimated approximately. However, conventional visual inspection is not only inefficient, but also fails to accurately calculate the regional distribution characteristics of the starch grains, which doubts the objectivity and scientificity of the nutritional quality assessment.
In order to solve the problems, the invention provides a method and a device for objectively and scientifically analyzing the quality of rice, namely starch grain distribution.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
According to one aspect of the present invention, there is provided a starch granule distribution analysis apparatus for analyzing an image of a section slice of dyed rice, the starch granule distribution analysis apparatus comprising: an image reconstruction module for extracting image components concerning starch grains based on color distribution of pixels in the image to create a starch grain distribution image; and a starch distribution determining module for determining the starch content distribution of the rice cross section based on the distribution of starch granule pixels in the starch granule distribution image.
Further, the image reconstruction module includes: the color analysis module is used for analyzing the color distribution of each pixel in the image to obtain a color characteristic value of each pixel; and an image recognition and extraction module for recognizing and extracting pixels which accord with a preset color characteristic value range in the image, and generating the starch granule distribution image by the extracted pixels according to the original position information.
Further, the starch granule distribution analysis device further includes: and the contour analysis module is used for carrying out binarization processing on the image to obtain the contour of the rice section.
Further, the starch distribution determining module calculates an area ratio of a sum of areas of starch granule pixels in a predetermined area of the rice cross section to the predetermined area as a starch content of the predetermined area.
Further, the starch distribution calculation module calculates the starch content in a preset radius range by taking the center of gravity point of the rice section as the center of a circle.
Further, the starch distribution calculation module includes: the image segmentation module is used for gridding the starch grain distribution image; the density calculation module is used for calculating the content in each grid; and a statistics module for summing the contents of the grids in a preset radius range taking the center of gravity point of the rice section as the center of a circle to obtain the starch content in the preset radius range.
According to an aspect of the present invention, there is provided a starch granule distribution analysis method for analyzing an image of a section slice of dyed rice, the starch granule distribution analysis method comprising: extracting image components concerning starch grains based on the color distribution of each pixel in the image to create a starch grain distribution image; and determining the starch content distribution of the rice cross section based on the distribution of starch granule pixels in the starch granule distribution image.
Further, the establishing a starch granule distribution image includes: analyzing the color distribution of each pixel in the image to obtain a color feature value of each pixel; and identifying and extracting pixels which accord with a preset color characteristic value range in the image, and generating the starch granule distribution image by the extracted pixels according to the original position information.
Further, the starch granule distribution analysis method further comprises the following steps: and carrying out binarization treatment on the image to obtain the profile of the rice section.
Further, the determining of the starch content distribution of the rice cross section includes calculating the area ratio of the sum of the areas of the starch granule pixels within a predetermined area of the rice cross section to the predetermined area as the starch content of the predetermined area.
Further, calculating the starch content of the predetermined area includes calculating the starch content within a predetermined radius range with the center of gravity of the rice cross section as the center of a circle.
Further, the calculating the starch content in the predetermined radius range by taking the center of gravity point of the rice section as the center of a circle comprises: gridding the starch grain distribution image; calculating the content in each grid; and adding the contents of the grids in a preset radius range taking the center of gravity point of the rice section as the center of a circle to obtain the starch content in the preset radius range.
According to one aspect of the present invention there is provided a starch granule distribution analysis apparatus comprising a processor and a memory coupled to the processor, the memory having stored thereon computer instructions which, when executed, implement the method of any of the above.
According to one aspect of the present invention, there is provided a starch granule distribution analysis system comprising: an optical imaging device for taking an image of a section slice of the stained rice; and a starch granule distribution analysis apparatus according to any one of the above.
Further, the optical imaging apparatus includes: an optical microscope including an eyepiece having a preset magnification and an objective lens for magnifying the rice section slice; and an optical sensor coupled to the optical microscope to image the enlarged rice sectional slice to obtain the image.
According to one aspect of the present invention, there is provided a starch granule distribution analysis system comprising: an optical imaging device for taking an image of a section slice of the stained rice; and the starch granule distribution analysis device comprising a processor and a memory.
Further, the optical imaging apparatus includes: an optical microscope including an eyepiece having a preset magnification and an objective lens for magnifying the rice section slice; and an optical sensor coupled to the optical microscope to image the enlarged rice sectional slice to obtain the image.
According to an aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
Drawings
The above features and advantages of the present invention will be better understood after reading the detailed description of embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily to scale and components having similar related features or characteristics may have the same or similar reference numerals.
FIG. 1 is a block diagram of an apparatus according to an embodiment of one aspect of the present invention;
FIG. 2 is a diagram of meshing data according to an embodiment of an aspect of the present invention;
FIG. 3 is a flow chart of an embodiment according to one aspect of the invention.
Detailed Description
The invention is described in detail below with reference to the drawings and the specific embodiments. It is noted that the aspects described below in connection with the drawings and the specific embodiments are merely exemplary and should not be construed as limiting the scope of the invention in any way.
According to an aspect of the present invention, there is provided a starch granule distribution analysis apparatus 100, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110 and a starch distribution determination module 120.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose. The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of each pixel in the image data, records positions of the pixels of the starch granules, and forms a starch granule distribution image by using the positions of the pixels.
The starch distribution determining module 120 determines a starch content distribution of the rice cross-section based on a distribution of starch granule pixels in the starch granule distribution image.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110 and a starch distribution determination module 120. The image reconstruction module 110 includes: a color analysis module 111, and an image recognition extraction module 112.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose. The color analysis module 111 analyzes the colors of the pixels in the image and obtains the color feature value of each pixel, and provides the color feature values of the pixels to the image recognition extraction module 112. The image recognition extraction module 112 screens the pixels according to the range of eigenvalues of the color of the starch grains to extract the pixels of the starch grains, and generates a starch grain distribution image based on the positions of the pixels.
The starch distribution determining module 120 determines a starch content distribution of the rice cross-section based on a distribution of starch granule pixels in the starch granule distribution image.
Further, the color characteristic values include, but are not limited to, color, saturation, brightness, i.e., HSV parameters.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110, a starch distribution determination module 120, and a profile analysis module 130.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose.
The contour analysis module 130 performs binarization processing on the image data to obtain the contour of the rice cross section.
The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of pixels within a contour range in the image data, records positions of the pixels of the starch granules, and reconstructs a starch granule distribution image by using the positions of the pixels.
The starch distribution determining module 120 determines the starch content distribution of the rice cross-section based on the distribution of starch granule pixels within the contour range in the reconstructed image data.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110 and a starch distribution determination module 120.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose. The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of each pixel in the image data, records positions of the pixels of the starch granules, and reconstructs a starch granule distribution image by using the positions of the pixels.
The starch distribution determining module 120 calculates the ratio of the area of the starch granule pixels in the predetermined area to the area of the predetermined area in the starch granule distribution image and uses the ratio as the starch content ratio of the rice cross section, and uses the distribution of the starch granule pixels in the starch granule distribution image as the starch granule distribution of the rice cross section.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110, a starch distribution determination module 120, and a profile analysis module 130.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose.
The contour analysis module 130 performs binarization processing on the image data to obtain the contour of the rice cross section.
The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of pixels in a contour range in the image data, records positions of the pixels of the starch granules, and reconstructs a starch granule distribution image by using the positions of the pixels.
The starch distribution determining module 120 calculates the area ratio of the starch granule pixels in the contour range in the starch granule distribution image to the area ratio of the contour range as the starch content ratio of the rice cross section, and takes the distribution of the starch granule pixels in the starch granule distribution image as the starch granule distribution of the rice cross section.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110 and a starch distribution determination module 120.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose. The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of each pixel in the image data, records positions of the pixels of the starch granules, and reconstructs a starch granule distribution image by using the positions of the pixels.
The starch distribution determining module 120 calculates the area ratio of the starch granule pixels in the predetermined area to the predetermined area in the starch granule distribution image and takes the area ratio as the starch content ratio of the rice cross section. And taking the distribution condition of the starch granule pixels in the starch granule distribution image as the starch granule distribution condition of the rice section, namely determining the gravity center point of the rice section, and calculating the starch content in a preset radius range by taking the gravity center point of the rice section as the circle center. Namely, the maximum radius of the cross section of the rice is 100 percent, and the starch content in the radius range of 0-10 percent, 10-20 percent, … percent and 100 percent is calculated as the distribution condition of starch grains. In the irregular or non-uniform density image, the calculation of the position of the center of gravity point belongs to a conventional algorithm, and is not described herein.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110, a starch distribution determination module 120, and a profile analysis module 130.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose.
The contour analysis module 130 performs binarization processing on the image data to obtain the contour of the rice cross section.
The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of pixels in a contour range in the image data, records positions of the pixels of the starch granules, and reconstructs a starch granule distribution image by using the positions of the pixels.
The starch distribution determining module 120 calculates the area ratio of the starch granule pixels in the contour range in the starch granule distribution image to the area ratio of the contour range as the starch content ratio of the rice cross section, uses the distribution condition of the starch granule pixels in the starch granule distribution image as the starch granule distribution condition of the rice cross section, namely, determines the center of gravity point of the rice cross section, and calculates the starch content in the predetermined radius range by taking the center of gravity point of the rice cross section as the circle center. Namely, taking the farthest distance from the center of gravity point to the outline of the rice section as the maximum radius, taking the distance of the maximum radius as 100%, and calculating the starch content in the radius range of 0-10%, 10% -20% …% -100% as the distribution condition of starch particles.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110 and a starch distribution determination module 120. The starch distribution determining module 120 includes a graph dividing module 121, a density calculating module 122, and a statistics module 123.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose. The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of each pixel in the image data, records positions of the pixels of the starch granules, and reconstructs a starch granule distribution image by using the positions of the pixels.
The graph dividing module 121 divides the reconstructed starch granule distribution image into a number of grids, as shown in fig. 2. The density calculation module 122 calculates the starch granule content in each grid. The statistics module 123 calculates the starch content within a predetermined radius range by taking the center of gravity point of the rice section as the center of a circle. I.e. the sum of the starch contents of all the grids whose distance from the center of the grid to the center of gravity point is within a predetermined radius is calculated as the starch content within the predetermined radius. For example, the maximum radius of the rice section is 100%, and the starch content of which the distance from the center of the grid to the center of gravity is in the radius range of 0-10%, 10% -20%, …% and 90% -100% is calculated as the distribution condition of starch particles.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110, a starch distribution determination module 120, and a profile analysis module 130. The starch distribution determining module 120 includes a graph dividing module 121, a density calculating module 122, and a statistics module 123.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose.
The contour analysis module 130 performs binarization processing on the image data to obtain the contour of the rice cross section.
The image reconstruction module 110 extracts blue-violet pixels, i.e. pixels of starch granules, by using color features of pixels in a contour range in the image data, records positions of the pixels of the starch granules, and reconstructs a starch granule distribution image by using the positions of the pixels.
The profile analysis module 130 performs binarization processing on the starch granule distribution image reconstructed by the image reconstruction module 110 to obtain the profile of the rice cross section.
The graph dividing module 121 divides the reconstructed starch granule distribution image into a number of grids, as shown in fig. 2. The density calculation module 122 calculates the starch granule content in each grid. The statistics module 123 calculates the starch content within a predetermined radius range by taking the center of gravity of the rice section as the center of a circle and the farthest distance from the center of gravity to the contour of the rice section as the maximum radius. I.e. the sum of the starch contents of all grids whose center-to-center distances are within a predetermined radius is calculated as the starch content within the predetermined radius. For example, the maximum radius of the rice section is 100%, and the starch content of which the distance from the center of the grid to the center of the circle is in the radius range of 0-10%, 10-20% … -100% is calculated as the distribution condition of starch particles. The statistics module calculates the ratio of the area of all starch granule pixels in the reconstructed image to the sum of the pixels of the image as the starch granule content of the rice section.
In one embodiment, as shown in fig. 1, the starch granule distribution analysis apparatus 100 includes an image reconstruction module 110, a starch distribution determination module 120, and a profile analysis module 130. The image reconstruction module 110 includes: a color analysis module 111, and an image recognition extraction module 112. The starch distribution determining module 120 includes a graph dividing module 121, a density calculating module 122, and a statistics module 123.
And (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. In these image data, starch granules in the section slice of rice after staining appear bluish violet, and other components such as cell walls and the like appear rose.
The contour analysis module 130 performs binarization processing on the image data to obtain the contour of the rice cross section.
The color analysis module 111 analyzes the colors of the pixels in the contour range in the image and obtains the color feature value of each pixel, and provides the color feature values of the pixels to the image recognition extraction module 112. The image recognition extraction module 112 screens the pixels according to the range of eigenvalues of the color of the starch grains to extract the pixels of the starch grains, and generates a starch grain distribution image based on the positions of the pixels.
Further, the color characteristic values include, but are not limited to, color, saturation, brightness, i.e., HSV parameters.
The graph dividing module 121 divides the reconstructed starch granule distribution image into a number of grids, as shown in fig. 2. The density calculation module 122 calculates the starch granule content in each grid. The statistics module 123 calculates the starch content within a predetermined radius range by taking the center of gravity of the rice section as the center of a circle and the farthest distance from the center of gravity to the contour of the rice section as the maximum radius. I.e. the sum of the starch contents of all grids whose center-to-center distances are within a predetermined radius is calculated as the starch content within the predetermined radius. For example, the maximum radius of the rice section is 100%, and the starch content of which the distance from the center of the grid to the center of the circle is in the radius range of 0-10%, 10-20% … -100% is calculated as the distribution condition of starch particles. The statistics module calculates the ratio of the area of all starch granule pixels in the contour range to the sum of the pixels in the contour range as the starch granule content of the rice section.
According to one aspect of the present invention, a starch granule distribution analysis method 300 is provided. As shown in fig. 3, the starch granule distribution analysis method 300 includes:
s310: image components for starch grains are extracted based on the color distribution of each pixel in the image to create a starch grain distribution image.
It will be appreciated that after staining the obtained slice of the rice cross section, some optical imaging device is used to obtain image data of the rice cross section slice. In these image data, starch granules in the stained rice section slice appear in a different color than other components of the rice section, such as the cell wall. Extracting the image component of starch grains according to the color of the starch grains after dyeing, and reconstructing a starch grain distribution image according to the written image component, wherein the image does not comprise images of other components of the rice section.
S320: and determining the starch content distribution of the rice cross section based on the distribution of starch granule pixels in the starch granule distribution image.
It will be appreciated that in the reconstructed starch granule distribution image, the pixel distribution of the starch granule is the starch granule content distribution of the rice cross section.
In one embodiment, as shown in fig. 3, the starch granule distribution analysis method 300 includes:
S311: the color distribution of each pixel in the image is analyzed to obtain a color characteristic value for each pixel.
It will be appreciated that after staining the obtained slice of the rice cross section, some optical imaging device is used to obtain image data of the rice cross section slice. In these image data, starch granules in the stained rice section slice appear in a different color than other components of the rice section, such as the cell wall. Analyzing the colors and obtaining a color characteristic value of each pixel.
Further, the color characteristic values include, but are not limited to, color, saturation, brightness, i.e., HSV parameters.
S312: and identifying and extracting pixels which accord with a preset color characteristic value range in the image, and generating the starch granule distribution image by the extracted pixels according to the original position information.
It will be appreciated that setting a range of color eigenvalues as a screening condition for pixels in the image data based on the color of the starch grains and the color eigenvalues of the color can screen out all the pixels of the starch grains, and reconstruct a starch grain distribution image based on the positional information of the pixels of the starch grains.
S320: and determining the starch content distribution of the rice cross section based on the distribution of starch granule pixels in the starch granule distribution image.
It will be appreciated that in the reconstructed starch granule distribution image, the pixel distribution of the starch granule is the starch granule content distribution of the rice cross section.
In one embodiment, as shown in fig. 3, the starch granule distribution analysis method 300 includes:
s330: and (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. And carrying out binarization processing on the image data to obtain the profile of the rice section.
S310: image components for starch grains are extracted based on the color distribution of each pixel in the image to create a starch grain distribution image.
In these image data, starch granules in the stained rice section slice appear in a different color than other components of the rice section, such as the cell wall. Extracting the image component of starch grains according to the color of the starch grains after dyeing, and reconstructing a starch grain distribution image according to the written image component, wherein the image does not comprise images of other components of the rice section.
S320: and determining the starch content distribution of the rice cross section based on the distribution of starch granule pixels in the starch granule distribution image.
It will be appreciated that in the reconstructed starch granule distribution image, the pixel distribution of the starch granule is the starch granule content distribution of the rice cross section.
In one embodiment, as shown in fig. 3, the starch granule distribution analysis method 300 includes:
s310: image components for starch grains are extracted based on the color distribution of each pixel in the image to create a starch grain distribution image.
It will be appreciated that after staining the obtained slice of the rice cross section, some optical imaging device is used to obtain image data of the rice cross section slice. In these image data, starch granules in the stained rice section slice appear in a different color than other components of the rice section, such as the cell wall. Extracting the image component of starch grains according to the color of the starch grains after dyeing, and reconstructing a starch grain distribution image according to the written image component, wherein the image does not comprise images of other components of the rice section.
S320: and determining the starch content distribution of the rice cross section based on the distribution of starch granule pixels in the starch granule distribution image.
It will be appreciated that in the reconstructed starch granule distribution image, the pixel distribution of the starch granule is the starch granule content distribution of the rice cross section. And calculating the area ratio of the sum of the areas of the starch granule pixels in the preset area to the area of the preset area in the image data of the rice section, and taking the area ratio as the starch content in the preset area.
Further, the predetermined area is an area within a predetermined radius range with the center of gravity of the rice cross section as the center of a circle.
In one embodiment, as shown in fig. 3, the starch granule distribution analysis method 300 includes:
s310: image components for starch grains are extracted based on the color distribution of each pixel in the image to create a starch grain distribution image.
It will be appreciated that after staining the obtained slice of the rice cross section, some optical imaging device is used to obtain image data of the rice cross section slice. In these image data, starch granules in the stained rice section slice appear in a different color than other components of the rice section, such as the cell wall. Extracting the image component of starch grains according to the color of the starch grains after dyeing, and reconstructing a starch grain distribution image according to the written image component, wherein the image does not comprise images of other components of the rice section.
S321: dividing the reconstructed starch grain distribution image into a certain number of grids;
s322: calculating the starch granule content in each grid, namely the proportion of starch granule pixels in the grid to all pixels in the grid;
s323: taking the center of gravity of the rice section as the center of a circle, calculating the sum of the starch grain contents in each grid within a preset radius range and taking the sum as the starch content within the preset radius range.
Wherein, the furthest distance from the center of gravity of the rice section to the edge is taken as the maximum radius, and the percentage of the radius is taken as the range, for example, the starch content in the range of 0-10%, 10% -20% …% -100% of the maximum radius is calculated.
Further, for the convenience of calculation, whether the center point of the mesh is within the predetermined radius is used as a criterion for distinguishing whether the mesh is within the predetermined radius. I.e. the sum of the starch contents of all the grids having a center electricity within the predetermined radius is calculated as the starch content within the predetermined radius.
In one embodiment, as shown in fig. 3, the starch granule distribution analysis method 300 includes:
s311: the color distribution of each pixel in the image is analyzed to obtain a color characteristic value for each pixel.
It will be appreciated that after staining the obtained slice of the rice cross section, some optical imaging device is used to obtain image data of the rice cross section slice. In these image data, starch granules in the stained rice section slice appear in a different color than other components of the rice section, such as the cell wall. Analyzing the colors and obtaining a color characteristic value of each pixel.
Further, the color characteristic values include, but are not limited to, color, saturation, brightness, i.e., HSV parameters.
S312: and identifying and extracting pixels which accord with a preset color characteristic value range in the image, and generating the starch granule distribution image by the extracted pixels according to the original position information.
It will be appreciated that setting a range of color eigenvalues as a screening condition for pixels in the image data based on the color of the starch grains and the color eigenvalues of the color can screen out all the pixels of the starch grains, and reconstruct a starch grain distribution image based on the positional information of the pixels of the starch grains.
S321: dividing the reconstructed starch grain distribution image into a certain number of grids;
s322: calculating the starch granule content in each grid, namely the proportion of starch granule pixels in the grid to all pixels in the grid;
s323: taking the center of gravity of the rice section as the center of a circle, calculating the sum of the starch grain contents in each grid within a preset radius range and taking the sum as the starch content within the preset radius range.
Wherein, the furthest distance from the center of gravity of the rice section to the edge is taken as the maximum radius, and the percentage of the radius is taken as the range, for example, the starch content in the range of 0-10%, 10% -20% …% -100% of the maximum radius is calculated.
Further, for the convenience of calculation, whether the center point of the mesh is within the predetermined radius is used as a criterion for distinguishing whether the mesh is within the predetermined radius. I.e. the sum of the starch contents of all the grids having a center electricity within the predetermined radius is calculated as the starch content within the predetermined radius.
In one embodiment, as shown in fig. 3, the starch granule distribution analysis method 300 includes:
s330: and (3) after the obtained slice of the rice section is dyed, acquiring image data of the rice section slice by adopting a plurality of optical imaging devices. And carrying out binarization processing on the image data to obtain the profile of the rice section.
S311: and analyzing the color distribution of each pixel in the outline in the image to obtain the color characteristic value of each pixel.
In these image data, starch granules in the stained rice section slice appear in a different color than other components of the rice section, such as the cell wall. Analyzing the colors and obtaining a color characteristic value of each pixel.
Further, the color characteristic values include, but are not limited to, color, saturation, brightness, i.e., HSV parameters.
S312: and identifying and extracting pixels which accord with a preset color characteristic value range in the image, and generating the starch granule distribution image by the extracted pixels according to the original position information.
It will be appreciated that setting a range of color eigenvalues as a screening condition for pixels in the image data based on the color of the starch grains and the color eigenvalues of the color can screen out all the pixels of the starch grains, and reconstruct a starch grain distribution image based on the positional information of the pixels of the starch grains.
S321: dividing the reconstructed starch grain distribution image into a certain number of grids;
s322: calculating the starch granule content in each grid, namely the proportion of starch granule pixels in the grid to all pixels in the grid;
s323: taking the center of gravity of the rice section as the center of a circle, calculating the sum of the starch grain contents in each grid within a preset radius range and taking the sum as the starch content within the preset radius range.
Wherein, the furthest distance from the center of gravity of the rice section to the contour is taken as the maximum radius, and the percentage of the radius is taken as the range, for example, the starch content in the range of 0-10%, 10% -20% …% -100% of the maximum radius is calculated.
Further, for the convenience of calculation, whether the center point of the mesh is within the predetermined radius is used as a criterion for distinguishing whether the mesh is within the predetermined radius. I.e. the sum of the starch contents of all the grids having a center electricity within the predetermined radius is calculated as the starch content within the predetermined radius.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood and appreciated by those skilled in the art.
According to one aspect of the present invention, there is provided a starch granule distribution analysis apparatus comprising a processor and a memory coupled to the processor. The memory stores computer instructions, and the processor implements any one of the starch granule distribution analysis methods when executing the computer instructions.
According to an aspect of the present invention, there is provided a starch granule distribution analysis system comprising an optical imaging device and any one of the starch granule distribution analysis devices described above. The optical imaging device is used for acquiring the image of the dyed rice section slice to be used as a processing object of the starch granule distribution analysis device.
Further, the optical imaging device includes an optical microscope and an optical sensor.
The optical microscope comprises an ocular lens with preset magnification and an objective lens, and is used for magnifying the section slice of the rice. It will be appreciated that the higher the magnification of the optical microscope, the smaller its field of view. Because the image data to be acquired in the invention has a certain resolution requirement, the optical microscope cannot observe the whole rice section under certain magnification, a series of partial images of the rice section are required to be shot, and then the images are spliced into a complete image of the rice section meeting the resolution requirement by a computer.
The optical sensor is coupled to the optical microscope for imaging an image of a section slice of rice displayed by the optical microscope to obtain an image of the section of rice.
According to one aspect of the present invention, a starch granule distribution analysis system is provided that includes an optical imaging device, a processor, and a memory coupled to the processor.
The optical imaging device is used for acquiring the image of the dyed rice section slice to be used as a processing object of the starch granule distribution analysis device.
The memory stores computer instructions, and the processor implements any one of the starch granule distribution analysis methods when executing the computer instructions.
Further, the optical imaging device includes an optical microscope and an optical sensor.
The optical microscope comprises an ocular lens with preset magnification and an objective lens, and is used for magnifying the section slice of the rice. It will be appreciated that the higher the magnification of the optical microscope, the smaller its field of view. Because the image data to be acquired in the invention has a certain resolution requirement, the optical microscope cannot observe the whole rice section under certain magnification, a series of partial images of the rice section are required to be shot, and then the images are spliced into a complete image of the rice section meeting the resolution requirement by a computer.
The optical sensor is coupled to the optical microscope for imaging an image of a section slice of rice displayed by the optical microscope to obtain an image of the section of rice.
According to an aspect of the present invention, there is provided a readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any one of the starch granule distribution analysis methods described above.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disk) as used herein include Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disk) usually reproduce data magnetically, while discs (disk) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (16)
1. A starch granule distribution analysis apparatus for analyzing an image of a section slice of dyed rice, the starch granule distribution analysis apparatus comprising:
an image reconstruction module for extracting image components concerning starch grains based on color distribution of pixels in the image to create a starch grain distribution image; and
a starch distribution determining module, configured to determine a starch content distribution of a rice cross section based on a distribution of starch granule pixels in the starch granule distribution image, wherein an area ratio of a sum of areas of starch granule pixels in a predetermined area of the rice cross section to the predetermined area is calculated as a starch content of the predetermined area.
2. The starch granule distribution analysis apparatus of claim 1, wherein the image reconstruction module comprises:
the color analysis module is used for analyzing the color distribution of each pixel in the image to obtain a color characteristic value of each pixel; and
the image recognition and extraction module is used for recognizing and extracting pixels which accord with a preset color characteristic value range in the image, and generating the starch granule distribution image by the extracted pixels according to the original position information.
3. The starch granule distribution analysis apparatus of claim 1, further comprising:
and the contour analysis module is used for carrying out binarization processing on the image to obtain the contour of the rice section.
4. The starch granule distribution analysis apparatus as set forth in claim 1, wherein said starch distribution determination module calculates the starch content within a predetermined radius range with the center of gravity of the rice cross section as the center of the circle.
5. The starch granule distribution analysis apparatus of claim 4, wherein the starch distribution determination module comprises:
the image segmentation module is used for gridding the starch grain distribution image;
the density calculation module is used for calculating the content in each grid; and
And the statistics module is used for summing the contents of the grids in a preset radius range taking the center of gravity point of the rice section as the center of a circle to obtain the starch content in the preset radius range.
6. A starch granule distribution analysis method for analyzing an image of a section slice of dyed rice, the starch granule distribution analysis method comprising:
extracting image components concerning starch grains based on the color distribution of each pixel in the image to create a starch grain distribution image; and
determining a starch content distribution of a rice cross-section based on a distribution of starch grain pixels in the starch grain distribution image, the determining the starch content distribution of the rice cross-section including calculating an area ratio of a sum of areas of starch grain pixels in a predetermined area of the rice cross-section to the predetermined area as a starch content of the predetermined area.
7. The starch granule distribution analysis method of claim 6, wherein said creating a starch granule distribution image comprises:
analyzing the color distribution of each pixel in the image to obtain a color feature value of each pixel; and
and identifying and extracting pixels which accord with a preset color characteristic value range in the image, and generating the starch granule distribution image by the extracted pixels according to the original position information.
8. The starch granule distribution analysis method of claim 6, further comprising:
and carrying out binarization treatment on the image to obtain the profile of the rice section.
9. The starch granule distribution analysis method of claim 6, wherein calculating the starch content of the predetermined area comprises calculating the starch content within a predetermined radius range with a center of gravity of a cross section of the rice as a center of circle.
10. The starch granule distribution analysis method of claim 9, wherein calculating the starch content within a predetermined radius range with the center of gravity of the rice cross section as the center of the circle comprises:
gridding the starch grain distribution image;
calculating the content in each grid; and
and adding the contents of all grids in a preset radius range taking the center of gravity point of the rice section as the center of a circle to obtain the starch content in the preset radius range.
11. A starch granule distribution analysis apparatus comprising a processor and a memory coupled to the processor, the memory having stored thereon computer instructions that, when executed, implement the method of any of claims 6-10.
12. A starch granule distribution analysis system, comprising:
An optical imaging device for taking an image of a section slice of the stained rice; and
the starch granule distribution analysis apparatus according to any one of claims 1 to 5.
13. The starch granule distribution analysis system of claim 12, wherein the optical imaging device comprises:
an optical microscope including an eyepiece having a preset magnification and an objective lens for magnifying the rice section slice; and
an optical sensor is coupled to the optical microscope to image the enlarged rice sectional slice to obtain the image.
14. A starch granule distribution analysis system, comprising:
an optical imaging device for taking an image of a section slice of the stained rice; and
the starch granule distribution analysis apparatus according to claim 11.
15. The starch granule distribution analysis system of claim 14, wherein the optical imaging device comprises:
an optical microscope including an eyepiece having a preset magnification and an objective lens for magnifying the rice section slice; and
an optical sensor is coupled to the optical microscope to image the enlarged rice sectional slice to obtain the image.
16. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 6-10.
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